Acoustic Characteristics Research Articles

HEAR set: A ligHtwEight acoustic paRameters set to assess mental health from voice analysis

BackgroundVoice analysis has significant potential in aiding healthcare professionals with detecting, diagnosing, and personalising treatment. It represents an objective and non-intrusive tool for supporting the detection and monitoring of specific pathologies. By calculating various acoustic features, voice analysis extracts valuable information to assess voice quality. The choice of these parameters is crucial for an accurate assessment. MethodIn this paper, we propose a lightweight acoustic parameter set, named HEAR, able to evaluate voice quality to assess mental health. In detail, this consists of jitter, spectral centroid, Mel-frequency cepstral coefficients, and their derivates. The choice of parameters for the proposed set was influenced by the explainable significance of each acoustic parameter in the voice production process. ResultsThe reliability of the proposed acoustic set to detect the early symptoms of mental disorders was evaluated in an experimental phase. Voices of subjects suffering from different mental pathologies, selected from available databases, were analysed. The performance obtained from the HEAR features was compared with that obtained by analysing features selected from toolkits widely used in the literature, as with those obtained using learned procedures. The best performance in terms of MAE and RMSE was achieved for the detection of depression (5.32 and 6.24 respectively). For the detection of psychogenic dysphonia and anxiety, the highest accuracy rates were about 75 % and 97 %, respectively. ConclusionsThe comparative evaluation was carried out to assess the performance of the proposed approach, demonstrating a reliable capability to highlight affective physiological alterations of voice quality due to the considered mental disorders.

Turning up the heat: Effects of temperature on agonistic acoustic communication in the two-spotted goby (Pomatoschistus flavescens)

Acoustic communication is linked to fitness traits in many animals, but under the current scenario of global warming, sound signals can be affected by rising temperatures, particularly in ectothermic organisms such as fishes. This study examines the effect of water temperature in acoustic communication in the two-spotted goby, Pomatoschistus flavescens. To address this, we looked at the effect of different temperatures on the acoustic features of drums produced by males during territorial defence and related it with their auditory sensitivity. We also analysed the differences in acoustic features between male agonistic drums and previously reported male courtship sounds, to better understand how acoustic communication may be affected by different temperature conditions. We recorded two-spotted goby males during territorial intrusions for 10 min at 16 °C, 19 °C, and 21 °C in the laboratory. We found that agonistic drums were shorter, had fewer pulses and shorter pulse periods at higher temperature, in contrast with the peak frequency that remained unaffected. Male agonistic and mating drums (recorded in a previous study) at 16 °C only differed in pulse period, which was higher in mating drums. Hearing thresholds obtained with Auditory Evoked Potentials at 16 °C, revealed higher sensitivity below 400 Hz, matching the main energy of agonistic and mating sounds. Our findings suggest that increasing temperature could potentially affect acoustic communication in this species by reducing the duration of agonistic drums, which might hinder effective communication. Nevertheless, the impact may not be significant as there was a good match between the best hearing sensitivity and the peak frequency range of their calls, which was not influenced by temperature. As fish and other organisms are increasingly threatened by multiple anthropogenic stressors, including warming, future research should address how changes in water temperature impact acoustic communication within a more realistic multi-stressor scenario.

Analysis of acoustic radiation problems involving arbitrary immersed media interfaces by the extended finite element method with Dirichlet to Neumann boundary condition

To quantify the influence of moving immersed media on acoustic radiation, this study develops an efficient method for acoustic radiation with arbitrary immersed media interfaces based on the extended finite element method (XFEM) and the Dirichlet-to-Neumann (DtN) boundary condition. The XFEM is employed for efficient and accurate modeling of the acoustic field with boundary shape variations. It requires no modification of the computational mesh and accurately captures non-smooth solutions on the interface by constructing enrichment functions. Additionally, the DtN boundary condition simulates the far-field radiation condition by establishing the relationship between the acoustic pressure and its derivatives. Numerical examples show that the proposed method efficiently characterizes changes in the position of immersed media interfaces without re-meshing the mesh. Variations in the thickness of porous material domains alter the acoustic radiation characteristics, with thicker porous material domains resulting in more pronounced noise reduction effects. Compared to changes in the thickness of porous material domains, changes in their position significantly alter the distribution of radiation pressure, indicating that ideal noise reduction effects can be achieved by strategically placing porous materials in specific locations in practical engineering applications.

Analysis of fracture modes and acoustic emission characteristics of low‐frequency disturbed coal rock bodies with different cyclic amplitudes

AbstractFrequent mining operations significantly disturb the security of deep weakly cemented rock roadways in western mining areas, constituting one of the primary causes of deformation, instability, and failure within the coal‐rock body. In this paper, dynamic uniaxial compression tests of soft rock‐coal combinations under low‐frequency disturbance with different cyclic amplitudes were conducted based on acoustic emission to elucidate fracture modes. The findings are as follows: Different cycle amplitudes manifested significant degradation effects on the soft rock‐coal combination. With increasing cycle amplitude, the proportion of tensile cracks initially decreased before subsequently increasing, demonstrating a general upward trajectory. The sudden increase in the acoustic emission RA value, the large decrease in the b‐value, and AE counts reaching the peak mean that failure and destabilization of the specimen begin to occur. The results of this study will furnish theoretical direction for dynamic disaster monitoring and early warning in soft rock mines located in western mining regions.

Fusion of PCA and ICA in Statistical Subset Analysis for Speech Emotion Recognition.

Speech emotion recognition is key to many fields, including human-computer interaction, healthcare, and intelligent assistance. While acoustic features extracted from human speech are essential for this task, not all of them contribute to emotion recognition effectively. Thus, reduced numbers of features are required within successful emotion recognition models. This work aimed to investigate whether splitting the features into two subsets based on their distribution and then applying commonly used feature reduction methods would impact accuracy. Filter reduction was employed using the Kruskal-Wallis test, followed by principal component analysis (PCA) and independent component analysis (ICA). A set of features was investigated to determine whether the indiscriminate use of parametric feature reduction techniques affects the accuracy of emotion recognition. For this investigation, data from three databases-Berlin EmoDB, SAVEE, and RAVDES-were organized into subsets according to their distribution in applying both PCA and ICA. The results showed a reduction from 6373 features to 170 for the Berlin EmoDB database with an accuracy of 84.3%; a final size of 130 features for SAVEE, with a corresponding accuracy of 75.4%; and 150 features for RAVDESS, with an accuracy of 59.9%.

Photoacoustic differential attenuation spectrum for bone assessment

Abstract Photoacoustic (PA) technology shows great potential in bone microstructure assessment. Due to the solid-liquid two-phase characteristics of cancellous bone, the PA signal received by transducers is aliasing of distributed PA source characteristics and acoustic propagation characteristics of cancellous bone. The contribution of various multi-scale distributed PA sources to the PA signal complexity leads to difficulty in identifying the sound propagation characteristics, making it difficult for quantitative assessment of bone microstructure. To eliminate the spectrum contribution of the PA source to the PA signal, we proposed an eccentric excitation-differential detection system and the acoustic propagation characteristics of cancellous bone can be obtained by calculating the PA differential attenuation spectrum (PA-DAS). The PA-DAS quantization parameter-slope is further extracted to quantify the attenuation of different frequency components. Simulation results show that slope strongly correlates to the porosity of cancellous bone, demonstrating the eccentric excitation-differential detection system combined with the PA-DAS method can realize the quantitative bone assessment.

Classification method of seabed sonar image substrate based on ELM-AdaBoost

The results of sediment classification are affected by the measurement environment. The water depth has a significant impact on the sound propagation, and the acoustic characteristics will be different under different water depths. In addition, seabed topography, sediment types and their distribution, acoustic frequency, equipment types and settings, and suspended substances in water will all affect the original sonar data. In order to obtain accurate classification results, a seabed sonar image classification method based on Elm AdaBoost is proposed. The original sound intensity data is compensated for gain and the anomaly is eliminated. After preprocessing the multi beam sonar data, the image texture feature is extracted from the gray level co-occurrence matrix as the feature vector for classification, and the elm classifier is constructed. The elm is enhanced by AdaBoost. The calculated feature is input into the elm AdaBoost network for sediment classification, and the result is output. Simulation results show that the proposed method effectively improves the accuracy of sediment classification, and verifies the feasibility of this method.

On the modulated fifth order dressed electron acoustic electrostatic and energy features in auroral plasma

In this work, the higher order electrostatic and solitonic EA energy plasma in auroral plasma has been investigated. The perturbed KdV form has been solved. The modulated soliton speed, energy of the cold beam electrons, and the associated higher fifth order electrostatic field have been mathematically derived. The effect of perturbed beam density as well as beam temperature parameters on the dressed electrostatic and energy properties has been discussed. The auroral plasma results in this work may be important in space applications.

Performance Analysis of Deep Learning based Signal Constellation Identification Algorithms for Underwater Acoustic Communications

This research delves into the evaluation of Deep learning signal constellation identification (DL-SCI) algorithms in underwater acoustic communications using Orthogonal Frequency Division Multiplexing (OFDM). It distinctly examines at how effective the recurrent neural networks (RNNs), particularly, Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) algorithms in predicting the signal constellation when applied to different underwater acoustic channels characteristics. Unlike manual feature selection in machine learning (ML), in this paper, DL-SCI exploits the labelled OFDM signals at the transmitter to detect and decode them at the receiver. In order to measure their effectiveness performance metrics, Bit Error Rate (BER) and parameters derived from the confusion matrix such as accuracy and precision are used. The study highlights the importance of utilizing zero cyclic prefix techniques which can exploit the inherent bandwidth limitation effectively. Furthermore, when examining complexity, it is observed that both GRU and LSTM algorithms require less floating-point operations (FLOPS) compared to traditional methods such as Minimum Mean Square Error (MMSE) and Least Squares (LS). Interestingly GRU shows performance in terms of complexity when compared to LSTM. Moreover, GRU outperforms LSTM by achieving a 4 dB improvement for long subcarriers. These results emphasize the effectiveness of learning techniques in enhancing performance and efficiency in acoustic communications.

Research on acoustic emission characteristics of metagabbros with different felsic development under splitting load

The acoustic emission (AE) characteristic signal can reveal the mechanical properties of rock materials and the development characteristics of internal microcracks. Rocks with different mineral development characteristics produce different AE signals during fracture. This study selected variable metagabbros with varying feldspathic development for AE tests under splitting load. The results demonstrated that the characteristics of AE ringing counts during the Brazilian fracture of metagabbro were closely correlated with the content of felsic minerals. The cumulative AE ringing count of metagabbros with feldspar nondevelopment exceeded 250 000, while those of metagabbros with feldspar development did not reach 200 000. As the feldspathic mineral content increases, the AE ringing counts of metagabbro exhibit an increasing trend in the high-energy (1e6–+∞ aJ) and high-amplitude (90–100 dB) intervals. With the development of feldspar minerals, the fracture mode of metagabbro gradually changed from shear failure to tensile failure. The higher the development of felsic minerals, the higher the stress level corresponding to the maximum fractal dimension, the greater the energy released by rock failure, and the more severe the damage. This study is of great significance for revealing the mechanism of rock rupture.

Investigation of porous surface on second mode in hypersonic boundary layer

The second mode is the main instability factor in hypersonic flow and shows the characteristics of acoustic disturbance. The stabilization effect of a porous surface on the second mode in a hypersonic boundary layer is studied according to the acoustic disturbance characteristics—including the flow characteristics of the porous surface, the effects of porous parameters, and the effect of mechanics—by direct numerical simulation. A rectangle is the basic structure of pores (slots) with a two-dimensional porous surface. The results show that the porosity (φ) and the number of pores (n) are the primary and secondary primary factors affecting the inhibition effect on the second mode by the porous surface. By influencing the amplitude and phase distribution of the pressure disturbance, the porous surface affects the wall normal transport between the mainstream and the porous surface in the near wall region, and it finally inhibits the growth of the second mode.

Acoustic emission spectrum characteristics of structural coal destruction in negative pressure environment

Abstract The uniaxial compression experiments under a low-pressure environment were performed by using structural coal samples. The frequency domain response characteristics of coal mass failure under loading in a low-pressure environment were acquired by FFT transformation and wavelet packet decomposition. The results show: As the loading stress of coal increases, the AE spectrum becomes more abundant, and the whole AE spectrum shows a left-shift trend. When the gas pressure increases, the acoustic emission signals change from low-frequency high-energy to high-frequency low-energy, the frequency band gradually narrates, and the spectrum changes from complex multi-peak shape to single-peak shape. As stress increases, the proportion of energy in the band 0-4.38 kHz gradually increases, while that in other bands gradually decreases. The energy response to stress changes in the two frequency bands of 2.92-4.38 kHz and 4.38-5.84 kHz is the most obvious. When the pressure changes, the energy in three frequency bands of 2.92-4.38 kHz, 4.38-5.84 kHz, and 7.3-8.76 kHz present an evident response trend with the pressure change, and the response trend (increase) of the latter two is exactly opposite (decrease) to that of the former. This phenomenon indicates that 2.92-4.38 kHz and 4.38-5.84 kHz are the characteristic frequency bands of the coal fracture process. The findings of this research offer crucial foundational data to support the monitoring and early warning of coal and gas outburst hazards.

High Sensitivity Performance of Piezoelectric Micromachined Ultrasonic Transducer Employing Sc-Doped AlN Thin Film

Abstract Aluminum Nitride (AlN) based piezoelectric micromachined ultrasonic transducers (PMUTs) have been the prime choice in acoustic imaging, because it has advantages of high frequency and thus high resolution. However, low piezoelectric constants of AlN gives rises to limitation in transmitting sensitivity. Scandium (Sc) doping method for AlN can be used to improve its piezoelectricity. In this paper, single element PMUT based on ScAlN thin film was proposed and modeled by performing finite element method (FEM). Influence of different electrode configurations on PMUT performance were studied. The results show that, compared with other electrode configurations, PMUT has the best overall performance when the upper electrode is platinum (Pt) and the lower electrode is molybdenum (Mo). In addition, the acoustic field characteristics and its dynamic analysis of PMUT based on ScAlN in water were studied. The investigation results demonstrated that the relative pulse-echo sensitivity level M of PMUT is significantly improved with the increase of Sc doping concentration in AlN thin film. When the Sc concentration is 40%, the M reaches -44 dB, which is 20 dB higher than that without Sc (-64 dB). The PMUT based on the high Sc-doped AlN thin film has a larger effective electromechanical coupling coefficient and sensitivity, which is beneficial to the application in high sensitivity ultrasound imaging.

Numerical investigation of the number of excitation elements-dependent acoustic field characteristics of a histotripter

Abstract Histotripsy is an emerging ultrasound technology for disintegrating various soft tissues noninvasively and accureately. However, it is difficult to characterize the acoustic field of a histotripter using the existing measurement methods (i.e., hydrophones) because of the damage potential of bubble cavitation on the sensing element under such high irradiation power. In this work, according to a quantitative relationship between the acoustic pressure and ultrasonic focusing gain G, nonlinearity β, attenuation coefficient α, a numerical reconstruction method is proposed to accurately and reliably estimate the acoustic field of a histotripter. The k-Wave toolbox was used to calculate the acoustic field produced from identical sectors on a common concave surface by gradually increasing the number of excitation elements (i.e., up to 256 that cover the whole surface). The excitation frequency is 0.8 MHz, and the initial pressure at the transducer surface is 0.4 MPa. The focusing gains, peak positive and negative pressure, positive to negative peak acoustic pressure ratio, and harmonic distributions in the focal region under different numbers of excited elements were analysed. It is found that when about a quarter of the transducer surface is excited (64 elements), the nonlinear effect becomes obvious, resulting in significant peak pressures and harmonics. Furthermore, the relationship between the positive and negative pressure peaks (p + and p ¬) and the number of excited elements fits a quadratic curve function quite well (R = 0.999). Altogether, the proposed method can be applied to estimate the acoustic pressure at an extremely high intensity that exceeds the range of current measurement apparatus. Acoustic pressures using a low number of excited elements are measured first and then extrapolated to the full surface coverage using the built-in regression models. In the near future, the field calculation scheme for the therapeutic focused transducers with higher irradiation power and larger dimensions, as well as experimental work, will be done to further validate our model.

Cavitation Characteristics in Ultrasonic Soldering and the Erosion Effect

In this work, the cavitation characteristics of the solder sonocapillary action during ultrasonic soldering were studied by a high-speed camera. The effects of ultrasonic power and time on the acoustic pressure, cavitation characteristics, and erosion effects were also investigated. Results showed that cavitation occurred throughout the entire sonocapillary action. Two kinds of bubble structures were observed. The steady bubbles had large sizes and long life periods. The transient bubbles had small sizes and short life periods. The steady bubbles preferentially appeared at the primary filling stage, while the transient bubbles dominated the entire cavitation field. The sonocapillary action was divided into the primary filling stage, the intermediate filling stage, and the completion filling stage. The primary filling stage had low acoustic pressure but high bubble density because the air content in the solder was high and some additional air entered the solder through the triple interface caused by ultrasonic vibration. Higher ultrasonic power resulted in stronger cavitation and, thus, better erosion effect on the substrate.

An experimental study of noise generated by tandem blades

To facilitate the low-noise design of tandem lift bodies as applied in aeroengines and aircraft, the acoustic features of tandem blades are investigated by wind-tunnel experiments. This is further specialized for the rotating blades applied in contra-rotating open rotors under the concept of frozen-rotor. A 70-channel phased microphone array and nine high-precision free-field microphones are employed. The beamforming method, enhanced by a source filtering technique, is employed to locate noise sources, providing insights into the source patterns of blade-blade interaction noise concerning flow speed, blade spacing, and aft blade clipping. The results show the following: (A) Sources of tandem-blade noise exist in the form of concentrated source clusters, resulting in two major clusters: the mid-span interaction noise and the tip-induced noise. (B) These source clusters tend to separate as flow speed or blade spacing increases. (C) By increasing blade spacing, the band-pass filtered overall sound pressure level is reduced by 2.9 dB. (D) A two-phase noise suppression pattern is observed with blade clipping, resulting in a total reduction of 3.0 dB for the interaction noise through the removal of tip-induced noise sources and the replacement of mid-span noise sources. Based on these findings, suggestions concerning blade spacing and clipping are discussed.

Acoustic feature-based emotion recognition and curing using ensemble learning and CNN

Emotion recognition and understanding plays a crucial role in various domains, including healthcare, human-computer interaction, and mental well-being. In this context, this paper proposes a methodology for recognizing and curing emotions using acoustic features and machine learning algorithms. The approach involves extracting acoustic features from the signals using diverse signal processing techniques. These features are then utilized as inputs for machine learning and deep learning algorithms, including the Random Forest classifier, XG Boost classifier, Convolutional Neural Network (CNN), and an ensemble algorithm. The ensemble algorithm combines Random Forest and XG Boost as base classifiers, with the Naïve Bayes algorithm serving as the meta classifier. We also propose a novel model that generates personalized curing strategies for individuals based on emotion recognition, so they can keep their emotional state positive. With the help of an ensemble learning model the proposed model achieved an emotion recognition accuracy of 92 % by combining three publicly available datasets containing emotional speech recordings. In the neutral and positive emotion classifications, the Receiver Operating Characteristic curve (ROC) had a 98 % accuracy rate while negative emotion classifications had a 91 % true positive rate. The effectiveness of the proposed curing methodology model has also been demonstrated by conducting experiments on a group of individuals and comparing the results with a state-of-the-art Generative Pre-Trained Transformer-3 (GPT-3) and ChatGPT, it was inferred that 89.35 % of the test group preferred the responses of the proposed curing model, over the GPT models The results of our experiments show that our proposed methodology can significantly boost the emotional state of an individual, thereby highlighting its potential for use in clinical settings.

Acoustic Feature Extraction Based on Wavelet Transform for Industrial Induction Motor-Driven Belt Conveyor Condition Monitoring

The industrial induction motor-driven belt conveyor is an essential component in manufacturing facilities. Any unexpected shutdown can lead to significant disruptions, resulting in financial losses amounting to thousands of dollars per hour. Unfortunately, efficient mechanisms for monitoring the conveyor's condition are often lacking. Therefore, it is crucial to ensure early, precise, and effective detection of malfunctions in belt conveyors. This necessitates the identification of distinctive anomalies stemming from initial damage to the rotating machinery and motor components. This paper presents a non-invasive acoustic monitoring technique designed specifically for industrial belt conveyors. The method employed relies on wavelet transform-based feature extraction, offering notable advantages in terms of classification accuracy, time efficiency, and the quantity of feature vectors required for classifier training.

Vocal labeling of others by nonhuman primates.

Humans, dolphins, and elephants are the only known species that vocally label their conspecifics. It remains unclear whether nonhuman primates share this ability. We recorded spontaneous "phee-call" dialogues between pairs of marmoset monkeys. We discovered that marmosets use these calls to vocally label their conspecifics. Moreover, they respond more consistently and correctly to calls that are specifically directed at them. Analysis of calls from multiple monkeys revealed that family members use similar calls and acoustic features to label others and perform vocal learning. These findings shed light on the complexities of social vocalizations among nonhuman primates and suggest that marmoset vocalizations may provide a model for understanding aspects of human language, thereby offering new insights into the evolution of social communication.

Occurrence and acoustic characteristics of finless porpoise (Neophocaena asiaeorientalis) off the south coast of Korea using hydrophone and visual surveys

Occurrence and acoustic characteristics of finless porpoise (Neophocaena asiaeorientalis) off the south coast of Korea using hydrophone and visual surveys